There are currently various efforts ongoing in relation to advertising in mobile communication systems. Compared to for example Internet advertising, in mobile communication networks there is the advantage that the identities of individual users are known whereby new opportunities arise for targeting advertisements to users.
For example a mobile network operator may operate as an advertising broker, which is an entity that maintains portfolios of advertisements to be delivered to end users. The advertisements may then be delivered to their recipients attached to connections or messages originating from other users or external systems and/or dedicated connections or messages may be used for delivering the advertisements.
While in some cases it may make sense to match advertisements with individual users, the more likely scenario is to target well defined micro segments (that is, groups of users). These segments are typically created using two different processes: static and dynamic profiling. In static profiling, the network operator uses traditional demographic segmentation, possibly amended with some user-expressed preferences, to segment the entire user base. In dynamic segmentation, the operator uses actual, real-time usage information gathered from the network to refine the static profiling.
Advertising campaigns are typically created using multiple selection criteria, such as target segment, target timeslot and/or target location and so forth. Based on these parameters, the advertisements content is then delivered to end users. The final segmentation of the user base is thus a result of both static and dynamic segmentation, meaning that there may be infinitely many segments and that an individual user may simultaneously belong to arbitrarily many segments. It is then challenge of the advertising system to deliver the advertisements to “right” recipients.
Google™ AdSense service is one prior art example of a system which tries to match advertisements with the content delivered to the recipients. The system is based on word analysis, that is, if certain word/words exist in the content that is delivered to the recipient (for example through a web page), delivery of certain advertisement/advertisements is triggered. A disadvantage of this system is that it sometimes attaches completely inappropriate or unrelated advertisements to the content. It may for example attach an advertisement of CD burners to a news clip that concerns a fire.
Another kind of a prior art system utilizes only static segmentation data to target the advertisements. For example, taking into account only basic demographic information like user's age, gender and billing address a system can make assumptions on generic categories of advertising suitable to the user. However, no user interest is captured in such static targeting model. A static targeting system can be further enhanced by including a user questionnaire to capture interests. Such questionnaires carry two drawbacks, though; users are generally reluctant to respond to long lists of questions and in addition their interests can change over time, making the initial responses invalid. Both incomplete responses and shifting interests result in poorly targeted campaigns.
Additionally, an advertising system may at the same time serve different advertisers that may have conflicting and/or competing campaigns, which may have at least partially overlapping target segments. That is, certain user may be included in target segment of various campaigns. It is however not desirable (neither for the advertisers nor the users) to deliver conflicting and/or competing campaigns to the same user at the same time. Moreover it is not desirable to overwhelm a user with a load of advertisements at the same time.
Thus an advertising system has the challenge to match suitable advertising content to the various segments in campaigns so that both the advertisers and the end users are best served. That is, the advertising system needs to have all advertising campaigns delivered to often very tightly defined target segments and at the same time to take care of that user experience does not deteriorate.
The existing systems do not always succeed in this very well. Thus, further improvement considerations are needed.